“Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design…”

Edward Tufte, The Visual Display of Quantitative Information, 1983

“It is that which gives to the view the great number of ideas in the shortest time with the least ink in the smallest space…”

Edward Tufte, The Visual Display of Quantitative Information, 1983

“It is nearly always multivariate…”

Edward Tufte, The Visual Display of Quantitative Information, 1983

“Graphical excellence requires telling the truth about the data…”

Edward Tufte, The Visual Display of Quantitative Information, 1983

• We will rely mostly on R for this course
• R can be downloaded from r-project.org

• ggplot2 is a powerful package for creating data visualizations
• It is built on the grammar of graphics
• It is a declarative grammar for data visualization

• git is a powerful tool for version control
• It allows you to
  • track changes to your code
  • revert to previous versions of your code
  • collaborate with others on your code
  • maintain multiple branches/versions of your code
  • and more

• git is not GitHub
• GitHub is a web-based platform for hosting and collaborating on code
• It is technically a remote repository for git
• It gives you a place to store your code and collaborate with others
• It is free for open source projects

• Notebooks are a powerful way to work with data and do data visualization
• They allow you to embed code, text, and visualizations in a single document
• They thus allow you to easily share both the process and the results of your work
• I do not require a specific notebook system for this course, but I will be using Quarto for examples

• Quarto is an open source scientific and technical publishing system
• You can create reports, websites, presentations, and books with Quarto
• This presentation is built with Quarto
• You can embed Python, R, and other code in your Quarto documents
• Quarto renders down to a document in HTML, PDF, or Word format; the files them selves are plain text
• Easy to store notebooks in version control with git

• RMarkdown is a way to create documents that mix R code and text
• It integrates with RStudio well and has a very similar workflow to Quarto
• RMarkdown renders down to a document in HTML, PDF, or Word format; the files them selves are plain text
• Easy to store notebooks in version control with git

• Jupyter is a notebook system popular with Python users
• Jupyter stores code and results in the same document (Quarto/RMarkdown render into a separate document)
• Jupyter supports R and other languages
• Jupyter stores itself as JSON (javascript object notation) files and are not as easy to diff in git

• RStudio is a powerful IDE for R
• It is free and open source
• It helps you understand what is in your environment (e.g., variables, functions, packages, etc.)
• It also makes it easy to view your visualizations as you make them

• Python is a powerful general purpose programming language
• It is very popular in the data science community, especially in machine learning

• LLMs are commonly used to help with code
• Common ones used in data science are ChatGPT, Claude, Gemini, and GitHub’s Copilot
• They can help you write code, debug code, and write documentation
• They can also make mistakes, so you cannot blindly trust their work